Upload 2 files

src/app.py

@@ -23,7 +23,9 @@ def load_model():
     model.classifier[3] = nn.Linear(num_ftrs, len(class_names))
 
     # Load the state dictionary
-    model_save_path = 'mobilenetv3_hymenoptera.pth'
+    # model_save_path = 'mobilenetv3_hymenoptera.pth'
+    file_dir = os.path.dirname(os.path.abspath(__file__))
+    model_save_path = os.path.join(file_dir, 'mobilenetv3_hymenoptera.pth')
     try:
         model.load_state_dict(torch.load(model_save_path, map_location=torch.device('cpu')))
         model.eval()

src/streamlit_app.py

@@ -23,7 +23,9 @@ def load_model():
     model.classifier[3] = nn.Linear(num_ftrs, len(class_names))
 
     # Load the state dictionary
-    model_save_path = 'mobilenetv3_hymenoptera.pth'
+    # model_save_path = 'mobilenetv3_hymenoptera.pth'
+    file_dir = os.path.dirname(os.path.abspath(__file__))
+    model_save_path = os.path.join(file_dir, 'mobilenetv3_hymenoptera.pth')
     try:
         model.load_state_dict(torch.load(model_save_path, map_location=torch.device('cpu')))
         model.eval()
@@ -51,7 +53,7 @@ model = load_model()
 st.title("Ant vs. Bee Classifier")
 st.write("Upload an image to classify whether it's an ant or a bee.")
 
-uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+uploaded_file = st.file_uploader("Choose an image ...", type=["jpg", "jpeg", "png"])
 
 if uploaded_file is not None:
     image = Image.open(uploaded_file).convert('RGB')